Overview

Brought to you by YData

Dataset statistics

Number of variables63
Number of observations438
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory215.7 KiB
Average record size in memory504.3 B

Variable types

Numeric12
Categorical51

Alerts

acousticness is highly overall correlated with energyHigh correlation
backing_instruments is highly overall correlated with style_RockHigh correlation
country_Andorra is highly overall correlated with loudness(dB)High correlation
danceability is highly overall correlated with happinessHigh correlation
energy is highly overall correlated with acousticness and 2 other fieldsHigh correlation
gender_Mix is highly overall correlated with main_singersHigh correlation
happiness is highly overall correlated with danceability and 1 other fieldsHigh correlation
loudness(dB) is highly overall correlated with country_Andorra and 1 other fieldsHigh correlation
main_singers is highly overall correlated with gender_MixHigh correlation
style_Rock is highly overall correlated with backing_instrumentsHigh correlation
country_Andorra is highly imbalanced (97.7%) Imbalance
country_Armenia is highly imbalanced (84.3%) Imbalance
country_Australia is highly imbalanced (89.6%) Imbalance
country_Austria is highly imbalanced (84.3%) Imbalance
country_Azerbaijan is highly imbalanced (81.9%) Imbalance
country_Belarus is highly imbalanced (83.1%) Imbalance
country_Belgium is highly imbalanced (80.7%) Imbalance
country_Bosnia and Herzegovina is highly imbalanced (91.0%) Imbalance
country_Bulgaria is highly imbalanced (84.3%) Imbalance
country_Croatia is highly imbalanced (83.1%) Imbalance
country_Cyprus is highly imbalanced (81.9%) Imbalance
country_Czech Republic is highly imbalanced (86.8%) Imbalance
country_Denmark is highly imbalanced (81.9%) Imbalance
country_Estonia is highly imbalanced (80.7%) Imbalance
country_Finland is highly imbalanced (80.7%) Imbalance
country_Georgia is highly imbalanced (84.3%) Imbalance
country_Greece is highly imbalanced (80.7%) Imbalance
country_Hungary is highly imbalanced (84.3%) Imbalance
country_Iceland is highly imbalanced (80.7%) Imbalance
country_Ireland is highly imbalanced (80.7%) Imbalance
country_Israel is highly imbalanced (83.1%) Imbalance
country_Latvia is highly imbalanced (81.9%) Imbalance
country_Lithuania is highly imbalanced (80.7%) Imbalance
country_Malta is highly imbalanced (80.7%) Imbalance
country_Moldova is highly imbalanced (81.9%) Imbalance
country_Montenegro is highly imbalanced (84.3%) Imbalance
country_Netherlands is highly imbalanced (81.9%) Imbalance
country_North Macedonia is highly imbalanced (81.9%) Imbalance
country_Norway is highly imbalanced (81.9%) Imbalance
country_Poland is highly imbalanced (83.1%) Imbalance
country_Portugal is highly imbalanced (85.5%) Imbalance
country_Romania is highly imbalanced (81.9%) Imbalance
country_Russia is highly imbalanced (84.3%) Imbalance
country_San Marino is highly imbalanced (84.3%) Imbalance
country_Serbia is highly imbalanced (83.1%) Imbalance
country_Slovakia is highly imbalanced (94.1%) Imbalance
country_Slovenia is highly imbalanced (81.9%) Imbalance
country_Sweden is highly imbalanced (83.1%) Imbalance
country_Switzerland is highly imbalanced (80.7%) Imbalance
country_Turkey is highly imbalanced (94.1%) Imbalance
country_Ukraine is highly imbalanced (84.3%) Imbalance
language_Mixed is highly imbalanced (55.2%) Imbalance
style_Opera is highly imbalanced (94.1%) Imbalance
style_Rock is highly imbalanced (63.1%) Imbalance
style_Traditional is highly imbalanced (59.8%) Imbalance
gender_Mix is highly imbalanced (57.4%) Imbalance
acousticness has 61 (13.9%) zeros Zeros
backing_dancers has 290 (66.2%) zeros Zeros
backing_singers has 277 (63.2%) zeros Zeros
backing_instruments has 297 (67.8%) zeros Zeros

Reproduction

Analysis started2024-10-22 09:35:14.597455
Analysis finished2024-10-22 09:35:49.278005
Duration34.68 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

semi_draw_position
Real number (ℝ)

Distinct19
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2945205
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:49.403052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q314
95-th percentile17
Maximum19
Range18
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.0970011
Coefficient of variation (CV)0.54838774
Kurtosis-1.1559081
Mean9.2945205
Median Absolute Deviation (MAD)4
Skewness0.028518578
Sum4071
Variance25.979421
MonotonicityNot monotonic
2024-10-22T11:35:49.561625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 26
 
5.9%
8 26
 
5.9%
10 26
 
5.9%
4 25
 
5.7%
5 25
 
5.7%
6 25
 
5.7%
7 25
 
5.7%
9 25
 
5.7%
12 25
 
5.7%
13 25
 
5.7%
Other values (9) 185
42.2%
ValueCountFrequency (%)
1 26
5.9%
2 24
5.5%
3 24
5.5%
4 25
5.7%
5 25
5.7%
6 25
5.7%
7 25
5.7%
8 26
5.9%
9 25
5.7%
10 26
5.9%
ValueCountFrequency (%)
19 4
 
0.9%
18 14
3.2%
17 20
4.6%
16 25
5.7%
15 25
5.7%
14 25
5.7%
13 25
5.7%
12 25
5.7%
11 24
5.5%
10 26
5.9%

main_singers
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3105023
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:49.724919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87132253
Coefficient of variation (CV)0.66487677
Kurtosis14.587233
Mean1.3105023
Median Absolute Deviation (MAD)0
Skewness3.6691683
Sum574
Variance0.75920295
MonotonicityNot monotonic
2024-10-22T11:35:49.884865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 364
83.1%
2 45
 
10.3%
3 13
 
3.0%
6 7
 
1.6%
4 6
 
1.4%
5 3
 
0.7%
ValueCountFrequency (%)
1 364
83.1%
2 45
 
10.3%
3 13
 
3.0%
4 6
 
1.4%
5 3
 
0.7%
6 7
 
1.6%
ValueCountFrequency (%)
6 7
 
1.6%
5 3
 
0.7%
4 6
 
1.4%
3 13
 
3.0%
2 45
 
10.3%
1 364
83.1%

key
Real number (ℝ)

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.36381
Minimum130.81
Maximum739.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:50.040356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum130.81
5-th percentile146.83
Q1196
median261.63
Q3386.4975
95-th percentile554.37
Maximum739.99
Range609.18
Interquartile range (IQR)190.4975

Descriptive statistics

Standard deviation142.13033
Coefficient of variation (CV)0.47006396
Kurtosis1.7040217
Mean302.36381
Median Absolute Deviation (MAD)87.02
Skewness1.342429
Sum132435.35
Variance20201.031
MonotonicityNot monotonic
2024-10-22T11:35:50.207319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
261.63 37
 
8.4%
246.94 28
 
6.4%
164.81 25
 
5.7%
220 25
 
5.7%
196 23
 
5.3%
146.83 23
 
5.3%
174.61 22
 
5.0%
392 22
 
5.0%
233.08 20
 
4.6%
293.66 20
 
4.6%
Other values (14) 193
44.1%
ValueCountFrequency (%)
130.81 18
4.1%
146.83 23
5.3%
164.81 25
5.7%
174.61 22
5.0%
196 23
5.3%
207.65 17
3.9%
220 25
5.7%
233.08 20
4.6%
246.94 28
6.4%
261.63 37
8.4%
ValueCountFrequency (%)
739.99 18
4.1%
622.25 3
 
0.7%
554.37 16
3.7%
493.88 5
 
1.1%
466.16 9
2.1%
440 19
4.3%
415.3 18
4.1%
392 22
5.0%
369.99 15
3.4%
349.23 15
3.4%

BPM
Real number (ℝ)

Distinct98
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.19863
Minimum53
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:50.899800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile77
Q197
median120
Q3130
95-th percentile158
Maximum187
Range134
Interquartile range (IQR)33

Descriptive statistics

Standard deviation24.308925
Coefficient of variation (CV)0.20920148
Kurtosis-0.2690697
Mean116.19863
Median Absolute Deviation (MAD)16
Skewness0.13234719
Sum50895
Variance590.92384
MonotonicityNot monotonic
2024-10-22T11:35:51.123753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128 23
 
5.3%
120 21
 
4.8%
130 20
 
4.6%
125 11
 
2.5%
132 11
 
2.5%
122 11
 
2.5%
138 10
 
2.3%
90 10
 
2.3%
126 9
 
2.1%
123 9
 
2.1%
Other values (88) 303
69.2%
ValueCountFrequency (%)
53 1
 
0.2%
66 1
 
0.2%
67 1
 
0.2%
69 1
 
0.2%
70 2
 
0.5%
72 3
0.7%
73 1
 
0.2%
74 1
 
0.2%
75 6
1.4%
76 4
0.9%
ValueCountFrequency (%)
187 1
 
0.2%
183 1
 
0.2%
180 1
 
0.2%
176 2
0.5%
174 1
 
0.2%
172 3
0.7%
170 4
0.9%
168 1
 
0.2%
164 2
0.5%
163 1
 
0.2%

energy
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.358447
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:51.337184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32.85
Q156
median71
Q383
95-th percentile93
Maximum100
Range91
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.927945
Coefficient of variation (CV)0.27689255
Kurtosis-0.054536417
Mean68.358447
Median Absolute Deviation (MAD)13
Skewness-0.70309856
Sum29941
Variance358.2671
MonotonicityNot monotonic
2024-10-22T11:35:51.544546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 16
 
3.7%
91 14
 
3.2%
70 12
 
2.7%
92 12
 
2.7%
67 12
 
2.7%
87 12
 
2.7%
82 12
 
2.7%
80 11
 
2.5%
64 10
 
2.3%
63 10
 
2.3%
Other values (68) 317
72.4%
ValueCountFrequency (%)
9 1
 
0.2%
10 1
 
0.2%
15 1
 
0.2%
18 2
0.5%
19 1
 
0.2%
20 3
0.7%
21 2
0.5%
26 1
 
0.2%
28 1
 
0.2%
29 2
0.5%
ValueCountFrequency (%)
100 1
 
0.2%
97 2
 
0.5%
96 6
1.4%
95 2
 
0.5%
94 9
2.1%
93 5
 
1.1%
92 12
2.7%
91 14
3.2%
90 6
1.4%
89 8
1.8%

danceability
Real number (ℝ)

High correlation 

Distinct69
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.780822
Minimum17
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:51.757650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30.85
Q146
median57
Q366
95-th percentile79.15
Maximum92
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.708064
Coefficient of variation (CV)0.26367599
Kurtosis-0.41232792
Mean55.780822
Median Absolute Deviation (MAD)10
Skewness-0.20668602
Sum24432
Variance216.32714
MonotonicityNot monotonic
2024-10-22T11:35:51.957135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 17
 
3.9%
52 16
 
3.7%
58 15
 
3.4%
63 14
 
3.2%
50 13
 
3.0%
56 13
 
3.0%
57 13
 
3.0%
53 12
 
2.7%
71 11
 
2.5%
61 11
 
2.5%
Other values (59) 303
69.2%
ValueCountFrequency (%)
17 2
0.5%
19 1
 
0.2%
20 1
 
0.2%
21 1
 
0.2%
22 1
 
0.2%
23 1
 
0.2%
25 1
 
0.2%
26 1
 
0.2%
27 4
0.9%
28 4
0.9%
ValueCountFrequency (%)
92 1
 
0.2%
89 1
 
0.2%
88 1
 
0.2%
87 2
 
0.5%
83 6
1.4%
82 2
 
0.5%
81 5
1.1%
80 4
0.9%
79 2
 
0.5%
78 2
 
0.5%

happiness
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.833333
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:52.172078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q128
median41
Q361
95-th percentile86
Maximum97
Range93
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.173068
Coefficient of variation (CV)0.49456656
Kurtosis-0.68268767
Mean44.833333
Median Absolute Deviation (MAD)16
Skewness0.4578008
Sum19637
Variance491.64493
MonotonicityNot monotonic
2024-10-22T11:35:52.408944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 11
 
2.5%
19 10
 
2.3%
32 10
 
2.3%
48 10
 
2.3%
31 9
 
2.1%
39 9
 
2.1%
36 9
 
2.1%
21 9
 
2.1%
28 9
 
2.1%
54 8
 
1.8%
Other values (78) 344
78.5%
ValueCountFrequency (%)
4 1
 
0.2%
7 1
 
0.2%
8 2
 
0.5%
9 3
0.7%
10 3
0.7%
11 2
 
0.5%
12 5
1.1%
13 2
 
0.5%
14 4
0.9%
15 4
0.9%
ValueCountFrequency (%)
97 2
0.5%
96 4
0.9%
93 2
0.5%
92 4
0.9%
89 4
0.9%
88 1
 
0.2%
87 4
0.9%
86 2
0.5%
85 3
0.7%
84 3
0.7%

loudness(dB)
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0981735
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:52.595835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14.25
median6
Q37
95-th percentile10
Maximum18
Range16
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.2654395
Coefficient of variation (CV)0.37149477
Kurtosis2.5644806
Mean6.0981735
Median Absolute Deviation (MAD)1
Skewness1.1792052
Sum2671
Variance5.1322163
MonotonicityNot monotonic
2024-10-22T11:35:52.777434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 92
21.0%
6 81
18.5%
4 79
18.0%
7 56
12.8%
8 42
9.6%
3 25
 
5.7%
10 20
 
4.6%
9 19
 
4.3%
11 8
 
1.8%
2 6
 
1.4%
Other values (5) 10
 
2.3%
ValueCountFrequency (%)
2 6
 
1.4%
3 25
 
5.7%
4 79
18.0%
5 92
21.0%
6 81
18.5%
7 56
12.8%
8 42
9.6%
9 19
 
4.3%
10 20
 
4.6%
11 8
 
1.8%
ValueCountFrequency (%)
18 1
 
0.2%
16 1
 
0.2%
15 1
 
0.2%
13 2
 
0.5%
12 5
 
1.1%
11 8
 
1.8%
10 20
 
4.6%
9 19
 
4.3%
8 42
9.6%
7 56
12.8%

acousticness
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.02968
Minimum0
Maximum89
Zeros61
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:52.984144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q333.75
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation24.301247
Coefficient of variation (CV)1.155569
Kurtosis0.29616858
Mean21.02968
Median Absolute Deviation (MAD)11
Skewness1.1950014
Sum9211
Variance590.5506
MonotonicityNot monotonic
2024-10-22T11:35:53.178386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
13.9%
1 48
 
11.0%
2 22
 
5.0%
12 19
 
4.3%
4 18
 
4.1%
5 13
 
3.0%
11 11
 
2.5%
14 10
 
2.3%
6 10
 
2.3%
3 10
 
2.3%
Other values (70) 216
49.3%
ValueCountFrequency (%)
0 61
13.9%
1 48
11.0%
2 22
 
5.0%
3 10
 
2.3%
4 18
 
4.1%
5 13
 
3.0%
6 10
 
2.3%
7 7
 
1.6%
8 8
 
1.8%
9 6
 
1.4%
ValueCountFrequency (%)
89 1
 
0.2%
87 3
0.7%
86 2
0.5%
85 2
0.5%
84 1
 
0.2%
83 3
0.7%
82 2
0.5%
81 1
 
0.2%
80 2
0.5%
79 1
 
0.2%

backing_dancers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89954338
Minimum0
Maximum5
Zeros290
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:53.337247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4631866
Coefficient of variation (CV)1.6265882
Kurtosis0.55464367
Mean0.89954338
Median Absolute Deviation (MAD)0
Skewness1.4034135
Sum394
Variance2.1409151
MonotonicityNot monotonic
2024-10-22T11:35:53.487268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
66.2%
4 41
 
9.4%
2 37
 
8.4%
1 36
 
8.2%
3 25
 
5.7%
5 9
 
2.1%
ValueCountFrequency (%)
0 290
66.2%
1 36
 
8.2%
2 37
 
8.4%
3 25
 
5.7%
4 41
 
9.4%
5 9
 
2.1%
ValueCountFrequency (%)
5 9
 
2.1%
4 41
 
9.4%
3 25
 
5.7%
2 37
 
8.4%
1 36
 
8.2%
0 290
66.2%

backing_singers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1004566
Minimum0
Maximum5
Zeros277
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:53.642075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6105913
Coefficient of variation (CV)1.4635664
Kurtosis-0.26382141
Mean1.1004566
Median Absolute Deviation (MAD)0
Skewness1.0971345
Sum482
Variance2.5940044
MonotonicityNot monotonic
2024-10-22T11:35:53.796327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 277
63.2%
3 48
 
11.0%
2 41
 
9.4%
4 36
 
8.2%
5 19
 
4.3%
1 17
 
3.9%
ValueCountFrequency (%)
0 277
63.2%
1 17
 
3.9%
2 41
 
9.4%
3 48
 
11.0%
4 36
 
8.2%
5 19
 
4.3%
ValueCountFrequency (%)
5 19
 
4.3%
4 36
 
8.2%
3 48
 
11.0%
2 41
 
9.4%
1 17
 
3.9%
0 277
63.2%

backing_instruments
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8196347
Minimum0
Maximum5
Zeros297
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-22T11:35:53.939062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.432502
Coefficient of variation (CV)1.7477323
Kurtosis1.5748897
Mean0.8196347
Median Absolute Deviation (MAD)0
Skewness1.6757211
Sum359
Variance2.0520621
MonotonicityNot monotonic
2024-10-22T11:35:54.100459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 297
67.8%
1 47
 
10.7%
2 27
 
6.2%
3 27
 
6.2%
4 23
 
5.3%
5 17
 
3.9%
ValueCountFrequency (%)
0 297
67.8%
1 47
 
10.7%
2 27
 
6.2%
3 27
 
6.2%
4 23
 
5.3%
5 17
 
3.9%
ValueCountFrequency (%)
5 17
 
3.9%
4 23
 
5.3%
3 27
 
6.2%
2 27
 
6.2%
1 47
 
10.7%
0 297
67.8%

qualified_10
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
1
257 
0
181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters438
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Length

2024-10-22T11:35:54.284996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:54.472614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring characters

ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

country_Andorra
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
437 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 437
99.8%
1.0 1
 
0.2%

Length

2024-10-22T11:35:54.669329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:54.865871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 437
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

country_Armenia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:35:55.036572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:55.194189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Australia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
432 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 432
98.6%
1.0 6
 
1.4%

Length

2024-10-22T11:35:55.381943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:55.531029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 432
98.6%
1.0 6
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

country_Austria
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:35:55.719613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:55.890157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Azerbaijan
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:35:56.106842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:56.267184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Belarus
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:35:56.420426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:56.570475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Belgium
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:35:56.741377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:56.882167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Bosnia and Herzegovina
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
433 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 433
98.9%
1.0 5
 
1.1%

Length

2024-10-22T11:35:57.045541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:57.193325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 433
98.9%
1.0 5
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

country_Bulgaria
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:35:57.346205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:57.501975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Croatia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:35:57.669101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:57.845889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Cyprus
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:35:58.017734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:58.163952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Czech Republic
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
430 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 430
98.2%
1.0 8
 
1.8%

Length

2024-10-22T11:35:58.324489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:58.473579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 430
98.2%
1.0 8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

country_Denmark
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:35:58.627672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:58.770459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Estonia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:35:59.066073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:59.269777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Finland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:35:59.432552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:59.605645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Georgia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:35:59.779167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:35:59.923292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Greece
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:00.111039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:00.278039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Hungary
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:36:00.441992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:00.580280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Iceland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:00.731598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:00.894154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Ireland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:01.075038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:01.287945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Israel
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:36:01.462581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:01.595598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Latvia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:01.744330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:01.888297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Lithuania
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:02.042076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:02.191391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Malta
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:02.341947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:02.492157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Moldova
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:02.668877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:02.816281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Montenegro
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:36:02.982628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:03.121567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Netherlands
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:03.272435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:03.414096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_North Macedonia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:03.584596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:03.742729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Norway
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:03.920775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:04.071865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Poland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:36:04.852866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:05.017234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Portugal
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
429 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 429
97.9%
1.0 9
 
2.1%

Length

2024-10-22T11:36:05.164448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:05.321441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 429
97.9%
1.0 9
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

country_Romania
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:05.472138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:05.621121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Russia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:36:05.768046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:05.932656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_San Marino
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:36:06.100532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:06.256824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Serbia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:36:06.427791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:06.574238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Slovakia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-22T11:36:06.742958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:06.884731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

country_Slovenia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-22T11:36:07.029784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:07.162532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Sweden
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-22T11:36:07.313970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:07.463544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Switzerland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-22T11:36:07.617426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:07.769435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Turkey
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-22T11:36:07.943410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:08.117658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

country_Ukraine
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-22T11:36:08.268224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:08.412700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

language_Mixed
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
397 
1.0
41 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 397
90.6%
1.0 41
 
9.4%

Length

2024-10-22T11:36:08.618423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:08.859560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 397
90.6%
1.0 41
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 835
63.5%
. 438
33.3%
1 41
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 835
63.5%
. 438
33.3%
1 41
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 835
63.5%
. 438
33.3%
1 41
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 835
63.5%
. 438
33.3%
1 41
 
3.1%

language_Native
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
356 
1.0
82 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 356
81.3%
1.0 82
 
18.7%

Length

2024-10-22T11:36:09.025580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:09.157784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 356
81.3%
1.0 82
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 794
60.4%
. 438
33.3%
1 82
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 794
60.4%
. 438
33.3%
1 82
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 794
60.4%
. 438
33.3%
1 82
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 794
60.4%
. 438
33.3%
1 82
 
6.2%

style_Dance
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
388 
1.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 388
88.6%
1.0 50
 
11.4%

Length

2024-10-22T11:36:09.305179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:09.456567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 388
88.6%
1.0 50
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

style_Opera
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-22T11:36:09.616947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:09.766964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

style_Pop
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
231 
1.0
207 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 231
52.7%
1.0 207
47.3%

Length

2024-10-22T11:36:09.914190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:10.066765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 231
52.7%
1.0 207
47.3%

Most occurring characters

ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

style_Rock
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
407 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 407
92.9%
1.0 31
 
7.1%

Length

2024-10-22T11:36:10.213372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:10.368313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 407
92.9%
1.0 31
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

style_Traditional
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
403 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 403
92.0%
1.0 35
 
8.0%

Length

2024-10-22T11:36:10.524627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:10.674750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 403
92.0%
1.0 35
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

gender_Male
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
254 
1.0
184 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 254
58.0%
1.0 184
42.0%

Length

2024-10-22T11:36:10.816084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:10.960092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 254
58.0%
1.0 184
42.0%

Most occurring characters

ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

gender_Mix
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
400 
1.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 400
91.3%
1.0 38
 
8.7%

Length

2024-10-22T11:36:11.106287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T11:36:11.253983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 400
91.3%
1.0 38
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Interactions

2024-10-22T11:35:46.350947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:25.436805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.426781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.114846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.837968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:32.935314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.679805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.468421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.255698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:41.477973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.060315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.725279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.502419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:25.591050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.579497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.268876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.999904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.076341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.831923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.619468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.410744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:41.617566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.200374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.863784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.641866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:25.985970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.709063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.406174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.148524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.216213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.975199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.755663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.565364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:41.755640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.342041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.997746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.772787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.131175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.846728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.553620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.301581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.354753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.110093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.897631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.716067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:41.886799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.470835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.127099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.913041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.272058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.981258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.713818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.459223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.504795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.254180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.043890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.869486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.020983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.609926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.252169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.044096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.394135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.108441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.840568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.608976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.651491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.400683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.177155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:39.000762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.140273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.745318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.376989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.200272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.543135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.253954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:29.986465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.775980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.800375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.561383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.340016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:39.145472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.281210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:43.895113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.534359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.346810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.680822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.388140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.117026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:31.915217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:33.941685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.719456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.490682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:39.289283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.400989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.028979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.667337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.497961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:26.857757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.530422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.272986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:32.381967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.097318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:35.875255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.665714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:39.461400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.554602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.191420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.814685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.634783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.002986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.671601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.411895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:32.527420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.243239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.018964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.808476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:39.662604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.675610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.314904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:45.947672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.768645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.136874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.823623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.557996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:32.669822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.395259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.161003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:37.951661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:40.484271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.804099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.450542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.089390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:47.913636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:27.276246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:28.974144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:30.704345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:32.800830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:34.548004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:36.310421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:38.105797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:40.671142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:42.933212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:44.587363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T11:35:46.224575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-22T11:36:11.459761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BPMacousticnessbacking_dancersbacking_instrumentsbacking_singerscountry_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainedanceabilityenergygender_Malegender_Mixhappinesskeylanguage_Mixedlanguage_Nativeloudness(dB)main_singersqualified_10semi_draw_positionstyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditional
BPM1.000-0.2280.077-0.0390.0090.0000.0000.0000.0470.0490.0000.1020.1590.0380.0000.0000.1060.0000.1910.0000.0410.0940.0000.0000.0980.0000.0000.0000.0000.0000.2050.0760.0000.0000.0000.2160.0270.0000.0000.1390.0000.0000.0000.0000.0000.0760.1100.2630.0000.0000.1040.0300.0000.090-0.1720.0150.0000.1210.2230.0000.1350.0650.130
acousticness-0.2281.000-0.174-0.061-0.0530.0000.0000.0000.0000.0000.0000.0630.1090.0000.1150.0000.0220.0000.0000.0000.0000.0000.0860.0000.0930.0610.0560.0000.0000.0000.0000.0000.0350.0000.0000.2020.0000.0970.0400.0750.1150.0000.0000.0700.0000.000-0.226-0.5200.0000.000-0.2660.0200.0000.2100.211-0.0200.0780.0180.2000.1010.2410.0740.056
backing_dancers0.077-0.1741.000-0.314-0.1250.0000.0000.0000.0000.1490.0000.0000.0000.0000.0550.1170.0000.0000.0000.0000.0000.0230.0000.0000.0370.0880.0000.0000.1400.2780.0770.0540.0000.1010.1130.0000.0000.0000.0000.0970.0000.0000.0120.0000.1710.1750.2620.2340.0000.0760.196-0.0450.2330.041-0.169-0.1470.056-0.0000.1400.0000.0000.1050.182
backing_instruments-0.039-0.061-0.3141.000-0.0570.1530.0000.0000.0000.0440.1000.0000.1780.1070.0000.0000.0840.1060.0000.1470.0510.0000.0640.0000.1580.0000.0600.0000.0640.0000.0000.0000.0000.0000.0100.0000.1070.0000.0000.0000.1810.0220.0280.1390.0280.000-0.0190.1240.1960.0000.073-0.0300.0710.139-0.057-0.0220.117-0.0370.0000.0000.0660.5220.000
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country_Bosnia and Herzegovina0.1590.1090.0000.1780.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.1000.2570.0000.0720.0000.0380.0000.0710.0000.0000.0000.0000.000
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country_Ireland0.0980.0930.0370.1580.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1250.0000.0000.0000.0000.0000.0470.0000.0000.0330.1710.0000.0000.0000.0000.000
country_Israel0.0000.0610.0880.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0830.0000.0000.0470.0000.000
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country_Lithuania0.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.1260.1120.0000.0000.1580.1120.0000.0000.0000.0000.0000.0000.000
country_Malta0.0000.0000.1400.0640.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.1090.1740.0000.0470.0000.0000.0000.1180.0000.0000.0000.0000.000
country_Moldova0.0000.0000.2780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.1250.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.000
country_Montenegro0.2050.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0750.0930.0360.0000.0000.0000.0000.000
country_Netherlands0.0760.0000.0540.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.2150.0000.0000.0710.0880.0000.0000.0000.0000.0000.0000.000
country_North Macedonia0.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.1200.1710.0000.0000.0000.0000.000
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country_Portugal0.2160.2020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.1670.0000.0000.0000.0000.0000.1930.3950.2190.0000.0000.0000.0000.0300.0000.094
country_Romania0.0270.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0460.0400.0000.0000.0000.0790.0810.0000.0000.0000.000
country_Russia0.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0670.0250.0000.0090.0660.0000.0000.0000.0000.0000.000
country_San Marino0.0000.0400.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.1260.0000.0000.0000.0230.0000.0560.0000.0440.0000.0000.0000.000
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country_Slovakia0.0000.1150.0000.1810.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0460.0000.1120.0520.0000.0000.0000.0170.0000.000
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country_Switzerland0.0000.0700.0000.1390.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0330.0520.0000.0000.0000.0000.000
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qualified_100.0000.0780.0560.1170.1140.0000.0000.0000.0000.0860.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0620.0170.0000.0330.0000.1200.0000.0000.0000.0930.0000.1200.0510.0000.0000.0000.0660.0560.0000.0520.0000.0770.0330.0000.1020.1210.0520.0520.0000.0880.0450.0000.0270.1140.0001.0000.1950.0370.0000.0790.0000.017
semi_draw_position0.1210.018-0.000-0.037-0.0280.0000.0730.0000.0730.0790.0000.0540.0710.0830.0250.0620.0000.0480.0000.0000.0710.0000.0000.0420.1710.0830.0500.0000.1180.0000.0360.0000.1710.0000.0330.0000.0790.0000.0000.0000.0000.0000.0000.0520.0000.021-0.0280.0050.0000.000-0.061-0.0100.0890.067-0.0550.0260.1951.0000.0590.0000.0000.0000.128
style_Dance0.2230.2000.1400.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0810.0000.0440.0000.0000.0000.0000.0000.0000.0000.1190.2650.0000.0000.2150.0650.0000.0530.0660.0000.0370.0591.0000.0000.3300.0700.079
style_Opera0.0000.1010.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2120.0000.0000.0000.1280.0340.0000.0000.0000.0000.0000.0000.0001.0000.0170.0000.000
style_Pop0.1350.2410.0000.0660.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0630.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0170.0000.0000.0000.0170.0000.2760.2030.0000.0000.2120.1080.0000.1850.0760.0000.0790.0000.3300.0171.0000.2480.267
style_Rock0.0650.0740.1050.5220.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.0000.1270.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.1830.0500.1050.0000.0000.0000.0210.0000.0000.0000.0700.0000.2481.0000.044
style_Traditional0.1300.0560.1820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.1380.0350.0000.0000.0870.1010.1740.2100.1410.1110.0170.1280.0790.0000.2670.0441.000

Missing values

2024-10-22T11:35:48.247031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-22T11:35:48.825768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

semi_draw_positionmain_singerskeyBPMenergydanceabilityhappinessloudness(dB)acousticnessbacking_dancersbacking_singersbacking_instrumentsqualified_10country_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainelanguage_Mixedlanguage_Nativestyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
011174.611007076456550000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.0
121246.941156975598100400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0
231261.63935668508900010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0
341349.23116184239118700010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.0
451164.811206168606000400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.01.00.00.01.00.0
561293.6610582833251412210.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.01.00.0
671293.66808452728300500.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.0
781146.838748321563900010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0
891261.6314288709634100510.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.01.00.0
9106233.0883346438116800010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0
semi_draw_positionmain_singerskeyBPMenergydanceabilityhappinessloudness(dB)acousticnessbacking_dancersbacking_singersbacking_instrumentsqualified_10country_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainelanguage_Mixedlanguage_Nativestyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
428101440.001329262402100400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.0
429111415.301259666814032000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0
430122311.1310895657121030010.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.0
431131369.991289265655440010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.0
432141220.0010358354086703110.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.0
433151369.99908657525441010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.0
434161207.651286768678032010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
435171369.991259261854150010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.0
436181174.611247269646302310.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0
437193415.301328966795403000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0